Energy-Efficient NoC-Based Systems for Real-Time Multimedia Applications using Approximate Computing


This work presents an energy-efficient NoC-based system for real-time multimedia applications employing approximate computing. The proposed video processing system, called SApp-NoC, is efficient in both energy and quality (QoS), employing a scalable NoC architecture composed of processing elements designed to accelerate the HEVC Fractional Motion Estimation (FME). Two solutions are proposed: HSApp-NoC (Heuristc-based SApp-NoC), and MLSApp-NoC (Machine Learning-based SApp-NoC). When compared to a precise solution processing 4K videos at 120 fps, HSApp-NoC and MLSApp-NoC reduce about 48.19% and 31.81% the energy consumption, at small quality reduction of 2.74% and 1.09%, respectively. Furthermore, a set of schedulability analysis is also proposed in order to guarantee the meeting of timing constraints at typical workload scenarios.
Palavras-chave: NoC, Approximate Computing, Machine Learning, Video Coding, Hardware Acceleration


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PENNY, Wagner I.; PALOMINO, Daniel M.; PORTO, Marcelo S.; ZATT, Bruno. Energy-Efficient NoC-Based Systems for Real-Time Multimedia Applications using Approximate Computing. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 34. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 7-12. ISSN 2763-8820. DOI: